Cognitively Tuned AI Bots for Linguistic Equity : A Neuro-Symbolic Framework for Policy-Aligned Language Learning Systems

Abstract

The accelerating deployment of AI-powered conversational agents across educational, healthcare, and civic domains presents a critical opportunity to address linguistic equity through cognitively tuned design. This abstract outlines a framework for developing AI bots that dynamically adapt to users' cognitive profiles, sociolinguistic contexts, and communicative goals to mitigate linguistic exclusion and foster inclusive digital engagement. Leveraging insights from neurocomputational linguistics, adaptive cognitive modeling, and participatory design, the proposed bots integrate user-specific semantic scaffolding, pragmatic alignment, and dialect-sensitive parsing to enable effective communication across diverse linguistic registers and abilities. These agents are grounded in ethical AI principles, emphasizing algorithmic transparency, intercultural sensitivity, and epistemic justice to counter biases that disproportionately affect minoritized language communities.

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